.png){kind=icon}
返回
A Novel Data Integration Framework Based on Unified Concept Model
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2672822.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nowadays, data is being generated, collected, and analyzed at an unprecedented scale, data integration is the problem of combining data from heterogeneous, autonomous data sources, and providing users with a unified view of integrated data. To design a data integration framework, we need to address challenges, such as schema mapping, data cleaning, record linkage, and data fusion. In this paper, we briefly introduce the traditional data integration approaches, and then, a novel graph-based data integration framework based on unified concept model (UCM) is proposed to address real-world refueling data integration problems. Within this framework, schema mapping was carried out and metadata from heterogeneous sources is integrated in a UCM. UCM has the benefits of being easy to update. It is also important for effective schema mapping and data transformation. By following the structure of UCM, data from different sources is automatically transformed into instance data and linked together by using semantic similarity computation metrics, finally the data is stored in graph database. Experiments are carried out based on heterogeneous data from refueling records, social networks of astroturfers, and vehicle trajectories. Experimental results and reference implementation demonstrations show good precision and recall of the proposed framework.
Keyword:
Data integration
schema mapping
graph model
semantic similarity computation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
A comparison of frozen and fresh platelet concentrates in the support of thrombocytopenic patients.png)
Transfusion
TransfusionIF0
PREAI
分享收藏
查看更多内容